Sold by: Applied Computing Research Labs
Deployed on AWS
SimKube is a simulation environment for Kubernetes used for cost forecasting, capacity planning, scalability analysis, or more.
Overview
SimKube is designed to allow users to simulate the behaviour of Kubernetes control plane components in a safe, isolated local environment. It is a "record-and-replay" simulator, which means that users can record the behaviour of a production cluster and then save that data for future analysis.
SimKube accomplishes this by running in a cluster with a real control plane; however, all pod behaviours are mocked out using Kubernetes WithOut Kubelet (KWOK). This means that anything that happens inside a pod can be simulated away. This enables users to simulate extremely large (1000+ node) Kubernetes clusters on a single EC2 instance.
The free version of the SimKube AMI includes a basic Kubernetes cluster running SimKube, along with all the tools you need to run and analyze your simulations!
Highlights
- Use SimKube to explore and optimize your Kubernetes clusters in a risk-free environment
- Perform cost forecasting or capacity planning by plugging in real instance type and pricing data from your cloud provider
- Simulate thousand-node Kubernetes clusters on a single EC2 instance
Details
Categories
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimensions summary
This is a free software listing. You pay no license fee for the software itself. Pricing runs by the hour and depends only on the AWS EC2 instance you choose to run it on. The dimensions cover two instance families: compute-focused (c6a, c6i, c7a, c7i) and general-purpose (m6a, m6i, m7a, m7i). Within each family, sizes scale from large up to 48xlarge. Larger sizes carry more CPU and memory, so hourly cost rises with size. You pick the instance that fits your Kubernetes simulation workload.
Top-of-mind questions for buyers
You pay only for the AWS EC2 instance that runs the software. The listing charges no license fee. Your hourly cost equals the running rate of the instance size and family you select. Charges accrue for each hour the instance runs.
Hourly charges apply while the instance runs. A fully stopped instance stops accruing hourly software rates. Stopped instances may still incur underlying AWS storage fees for attached volumes. The metering tracks running instance-hours only.
Compute-focused families (c6a, c6i, c7a, c7i) weight toward CPU capacity relative to memory. General-purpose families (m6a, m6i, m7a, m7i) balance CPU and memory. Choose compute-focused for CPU-heavy simulations. Choose general-purpose when your Kubernetes simulation needs more memory per core.
simkube.dev
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Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Additional details
Usage instructions
To use this AMI, first launch a supported AWS EC2 instance with your chosen configuration. You will need SSH access to the instance: a security group that allows access over port 22 and either a public IP address or a way to access hosts inside your private VPC. The SSH username is ubuntu, and you will need a keypair configured on the host in order to access it.
The launched instance will have a running kind cluster with SimKube configured; you can use kubectl to inspect the state of this cluster and confirm that it works correctly. The launched instance also has the skctl CLI tool installed for interacting with SimKube.
You can now follow the instructions on https://simkube.dev/simkube/docs/intro/running/ to run your first simulation. If you have a trace file already, you can place it in /var/kind/cluster, and then use skctl run to start the simulation. If you don't have a trace file already, you can find some example traces at https://github.com/acrlabs/simkube/tree/main/examples/traces
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Support
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Find a bug? File an issue on GitHub:
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